Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/117756
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dc.contributor.authorNavarro, D.J.-
dc.contributor.authorKemp, C.-
dc.date.issued2017-
dc.identifier.citationPsychological Review, 2017; 124(5):643-677-
dc.identifier.issn0033-295X-
dc.identifier.issn1939-1471-
dc.identifier.urihttp://hdl.handle.net/2440/117756-
dc.description.abstractEvery time we encounter a new object, action, or event, there is some chance that we will need to assign it to a novel category. We describe and evaluate a class of probabilistic models that detect when an object belongs to a category that has not previously been encountered. The models incorporate a prior distribution that is influenced by the distribution of previous objects among categories, and we present 2 experiments that demonstrate that people are also sensitive to this distributional information. Two additional experiments confirm that distributional information is combined with similarity when both sources of information are available. We compare our approach to previous models of unsupervised categorization and to several heuristic-based models, and find that a hierarchical Bayesian approach provides the best account of our data.-
dc.description.statementofresponsibilityDaniel J. Navarro, Charles Kemp-
dc.language.isoen-
dc.publisherAmerican Psychological Association-
dc.rights© 2017 American Psychological Association-
dc.subjectCategorization; novelty detection; Bayesian models-
dc.titleNone of the above: a Bayesian account of the detection of novel categories-
dc.typeJournal article-
dc.identifier.doi10.1037/rev0000077-
dc.relation.granthttp://purl.org/au-research/grants/arc/FT110100431-
pubs.publication-statusPublished-
dc.identifier.orcidNavarro, D.J. [0000-0001-7648-6578]-
Appears in Collections:Aurora harvest 8
Psychology publications

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